EP4287210A1 - Gerät, computergestütztes verfarhen, medizinisches system zur bestimmung eines vorhergesaten glykämiewerts - Google Patents

Gerät, computergestütztes verfarhen, medizinisches system zur bestimmung eines vorhergesaten glykämiewerts Download PDF

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EP4287210A1
EP4287210A1 EP22177106.6A EP22177106A EP4287210A1 EP 4287210 A1 EP4287210 A1 EP 4287210A1 EP 22177106 A EP22177106 A EP 22177106A EP 4287210 A1 EP4287210 A1 EP 4287210A1
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Prior art keywords
time
temporal
data
glycemia
predictive model
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EP22177106.6A
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English (en)
French (fr)
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Alice ADENIS
Maxime Louis
Hector Romero-Ugalde
Laurent DAUDET
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Diabeloop SA
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Diabeloop SA
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Priority to EP22177106.6A priority Critical patent/EP4287210A1/de
Priority to US18/324,716 priority patent/US20230395266A1/en
Publication of EP4287210A1 publication Critical patent/EP4287210A1/de
Pending legal-status Critical Current

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor

Definitions

  • the present invention relates to a device and method for determining a glycemia cyclic temporal behaviour over a given cyclic period of time, and a medical system including such device.
  • a processor is programmed to evaluate a volume rate of insulin to be injected, based on patient-related data and/or time-based data, such as past and/or present measures of glycemia, and to control the injection of insulin based on this evaluation.
  • the processor can be programmed to evaluate a volume of insulin to be injected in some special circumstances, in particular meals, and/or physical activity. The quantity can be injected to the patient, subject to the patient's approval.
  • Such systems are also called “semi closed-loop" systems because of the necessary declaration by the patient of some of these special circumstances.
  • the time-based data is often used to predict the future concentration of glycemia. This prediction is then used to calculate the quantity of insulin having to be injected in order to maintain the concentration of blood glucose in acceptable intervals.
  • An incorrect prediction of the future blood glucose can lead to an irrelevant calculated quantity of insulin to be injected, leading to a concentration of blood glucose in unacceptable intervals, where the patient may be in hypoglycemia and/or hyperglycemia.
  • the method predicts a future glucose value with a machine learning model trained to predict future glucose levels based on prior CGM (Continuous Glucose Monitoring).
  • the method of this paper predicts glucose values at intervals of 15 and 60 minutes by a machine learning model that has been trained with time-based data being a sliding time window of glucose values preceding the predicted values at a fixed interval.
  • One main drawback of this method is that the method does not enable taking into account a general behaviour of the glycemia, influenced, for example, by the patient's habit. This leads to a prediction of a future glycemia lacking accuracy.
  • One main drawback of this method is that the method does not enable taking into account a general behaviour of the glycemia, influenced, for example, by the patient's habit. This leads to a prediction of a future glycemia lacking accuracy.
  • the invention thus aims to answer at least partially the above presented technical problems.
  • the invention relates to a device for determining a predicted value of glycemia, the device including a data processing unit adapted to process time-based data, wherein:
  • the temporal predictive model is able to take into account a general behaviour of the glycemia, influenced, for example, by the patient's habit. This leads to an accurate prediction of a future glycemia.
  • At least a part of the encoded temporal data is encoded according to a linear distribution over the given cyclic period of time according to a predetermined constant time step.
  • the injected encoded data may be a time point in the time interval, the time point corresponding to the present time, i.e. the time the encoded temporal data is injected in the predictive temporal model.
  • the temporal predictive model has access to all the information needed to learn the glycemia tendency over the given period of time and hence predict an accurate future value of glycemia.
  • At least a part of the encoded temporal data is encoded according to an angular distribution over the given cyclic period of time according to a predetermined constant angle interval.
  • the predictive temporal model could have difficulty understanding the relationship between two consecutive given cyclic periods of time due to the discontinuous encoding.
  • the angular encoding of the encoded temporal data may use the sine and cosine functions, since the sine and cosine functions constitute a continuous and periodic isometry of the cyclic periods of time. That way, the encoding fully preserves distances between any two times in the cyclic periods. This embodiment then constitutes a more principled encoding of the time information.
  • the time-based data includes at least one past glycemia value.
  • the injection of past glycemia value and the encoded temporal data are sufficient for the temporal predictive model to output accurate predicted glycemia values.
  • the temporal predictive model is further configured to receive as an input:
  • the injection of the IOB(t) and the COB(t) in the temporal predictive model may help enhance even more the value of the predicted glycemia.
  • the given cyclic period of time is a day, a week and/or a year.
  • the encoded temporal data is tagged according to:
  • Such configuration may allow the temporal predictive model to take into account behavioural properties during the given cyclic period. For instance:
  • the present application also relate to a computerized method for determining a predicted value of glycemia, said method including a data processing unit processing at least time-based data, wherein:
  • the method includes a prior learning phase including:
  • the learning phase of the temporal predictive model with a plurality of time-based data, from a plurality of persons may increase the robustness of the temporal predictive model since it may be able to learn a plurality of different glycemia variations and behaviours.
  • the method includes a prior learning phase including:
  • the present application also relates to a medical system for regulating a glycemia of a person including:
  • the medical device further includes:
  • Such configuration allows the temporal predictive model, once trained, to receive the at least time-based data of the patient wearing the medical device, such that the temporal predictive model can be used to help patients treating their diabete.
  • the device is embedded in the medical device.
  • the present application also relates to a computer program including instructions which, when the program is executed by a computer, cause the computer to carry out a determination of a predicted value of glycemia of a device including a data processing unit adapted to process time-based data, wherein: ⁇ the data processing unit is configured to implement a temporal predictive model trained to learn a glycemia cyclic temporal behaviour, where said glycemia cyclic temporal behaviour is defined by variations of glycemia values in a cyclic way over a given cyclic period of time; wherein the temporal predictive model is configured to:
  • Figure 1 shows a medical system 1.
  • the medical system 1 is advantageously used in the field of diabetes.
  • the medical system 1 typically includes a data acquisition system 2, a data processing unit 3 and a drug dispensing system 4, notably an insulin dispensing system 4.
  • the medical system 1 is a computerized medical system 1.
  • the data processing unit 3 is a computerized data processing unit.
  • the data acquisition system 2 includes a computerized data acquisition system.
  • the insulin dispensing system 4 may be a computerized insulin dispensing system 4, including a processor which performs a process based on input parameters.
  • Any computerized system may include a processing subsystem, a storage subsystem, a user interface, a communication subsystem and a power subsystem.
  • a computerized system may also include other components such as power battery, connectors and so on (not explicitly shown).
  • a storage subsystem can store data.
  • a storage subsystem can also store one or more computer programs to be executed by a processing system or a processing subsystem.
  • the medical system 1 may also include a user interface (not shown).
  • the patient may be able to enter some data through the user interface, as will be described in more details below.
  • the user interface may also be configured to deliver outputs to the patient, such as notifications.
  • Examples of user interface may include a screen, speakers, etc.
  • the data acquisition system 2 of the medical system 1 is adapted to measure a parameter of the user such as time-based data.
  • the data acquisition system 2 may be a Continuous Glucose Monitoring (CGM) sensor.
  • CGM Continuous Glucose Monitoring
  • the data acquisition system 2 might be wearable by a patient.
  • the data acquisition system 2 is adapted to repeatedly measure time-based data of the patient. In an example, the measure is made at each time step, the time step being between 1 minute and 15 minutes, preferably every 5 minutes.
  • Time is associated with the measured data by means of a clock of the data acquisition system 2.
  • a data acquisition system may be called a continuous data acquisition system.
  • the data acquisition system 2 may be adapted to perform in vivo measurements
  • the time-based data may be a past and/or present concentration of glucose, also called glycemia.
  • the data acquisition system 2 includes a data communication module 21 adapted to communicate data to the data processing unit 3.
  • the data communication module 21 might be adapted to communicate wirelessly, for example using a short-range radio communication corresponding to the Bluetooth ® standard or BLE standard.
  • the data processing unit 3 of the medical system 1 may include a computerized system 31 for communicating treatment information to a user.
  • Said computerized system 31, as shown on figure 2 includes a processor 32 and a memory 33.
  • the data processing unit 3 includes a data communication module 34 adapted to communicate data, i.e. to receive and transmit data.
  • the data communication module 34 might be adapted to communicate wirelessly, for example using a short-range radio communication corresponding to the Bluetooth ® standard.
  • the data communication module 34 of the terminal 3 and the data communication module 21 of the data acquisition system 2 are compatible with one another, to form a communication channel, by which the data processing unit 3 and the data acquisition system 2 communicate with one another.
  • time-based data is communicated from the sensing unit 2 to the data processing unit 3.
  • the time-based data may be stored in the memory 22.
  • the medical system 1 further includes an insulin dispensing system 4.
  • the insulin dispensing system 4 includes a container 41 configured to contain insulin. Typically, the container 41 is able to contain a plurality of insulin doses, so as to be used a plurality of times.
  • the insulin dispensing device 4 further includes a dispensing unit 42 adapted to dispense drug to the user. It for example includes a needle to be inserted into the body of the user, and a plunger 43 which can be pushed to expel insulin from the container 41.
  • the insulin dispensing system 4 may further include a setting unit 44.
  • the setting unit 44 can be set by the user in order to control the amount of insulin which may be injected at a time using the insulin dispensing device 4.
  • the setting unit 44 may be set by the user to dispense a certain amount of insulin.
  • the dispensing system is then actuated to dispense the set amount of insulin and is prevented from operating more insulin than the set amount.
  • a settable mechanical stop for the plunger 8 may be used.
  • the setting unit 44 is the user interface, through which the patient is able to set an amount of insulin.
  • the insulin dispensing system 4 includes a communication interface (not shown) able to communicate with the user interface.
  • the data acquisition system 2, the data processing unit 3 and the insulin dispensing system 4 may communicate to each other through wireless communication, such as the Bluetooth standard. In another embodiment, they communicate with each other through wired connections.
  • the data acquisition system 2, the data processing unit 3, the insulin dispensing system 4 and the user interface may communicate to each other through wireless communication, such as the Bluetooth standard. In another embodiment, they communicate with each other through wired connections.
  • the data processing unit 3 could be integrated within the same unit as the data acquisition system 2 in a medical device, wearable by a patient.
  • the data processing unit 3 could be integrated within the same unit as the insulin dispensing device 4, in a medical device wearable by a patient.
  • the data acquisition system 2 could be integrated within the same unit as the insulin dispensing device 4.
  • the data acquisition system 2, the processing system 3 and the insulin dispensing device 4 may be integrated within the same medical device wearable by a patient.
  • the data acquisition system 2, the data processing unit 3, the insulin dispensing system 4 and the user interface may be integrated within the same medical device wearable by a patient.
  • Figure 2 more specifically shows an example of the data processing unit 3.
  • the data processing unit 3 is configured to, at least, process data acquired by the data acquisition system 2.
  • the data processing unit 3 includes a computerized system 31 including a processor 32 and a memory 33 storing data.
  • the memory stores time-based data, acquired at least in part by the data acquisition system 2.
  • the time-based data acquired by the data acquisition system 2 are transmitted to the memory 33 of the computerized system 31, via the data communication interface 34.
  • the memory 33 may also store patient-related data and other possible data, as meal-related data. Such data may be transmitted by the patient, via the user interface (not shown).
  • the data-processing system 3 is a computerized system which operates data-processing according to an artificial-intelligence-based scheme.
  • the processor 32 implements a temporal predictive model, designated as TPM on figure 2 .
  • the temporal predictive model is trained to determine a glycemia cyclic temporal behavior so as to be able to predict a future value of glycemia.
  • the trained temporal predictive model may be stored in the memory 33.
  • the result of the data-processing is an output.
  • the output can include a predicted value of the glycemia, i.e. a value of the glycemia at a future time. More particularly, the output can include a predicted value of the glycemia of the patient using the medical system.
  • the outputted predicted value of glycemia can be processed by a determination module 35 of the processor 32, configured to determine an insulin dose to be injected to the patient, based on the predicted value of glycemia at the future time. Such determined insulin dose may then be used as a control parameter to the insulin dispensing system 4.
  • the determined insulin dose may be subjected to the patient's setting, as described above.
  • the implemented temporal predictive model may be a machine-learning model or a deep-learning model.
  • the temporal predictive model may use a recurrent neural network.
  • the temporal predictive model is trained to determine a glycemia cyclic temporal behavior over a predetermined cyclic period of time and to deliver, as an output, a predicted value of glycemia.
  • the temporal predictive model may receive, as input, at least one time-based data.
  • Such a configuration allows the predictive model to accurately predict a glycemia taking into account a general behaviour of the glycemia, influenced, for example, by the patient's habit.
  • the time-based data includes, at least, past and/or present values of the glycemia of the patient.
  • the past and/or present values of the glycemia are past and/or present regarding the time the prediction of the future glycemia is outputted.
  • the time-based data may also include insulin-on-board IOB(t) which is a variable representative of the time variation of the patient's quantity of insulin on board, and carbs-on-board COB(t) which is a variable COB(t) representative of the time variation of the patient's quantity of carbohydrate on board.
  • IOB(t) insulin-on-board
  • COB(t) carbs-on-board COB(t) which is a variable COB(t) representative of the time variation of the patient's quantity of carbohydrate on board.
  • time-based data may be taken into account and may allow the temporal predictive model to be more accurate, as long as the data contains information relative to the current physiology of the patient.
  • the sensitivity to insulin defines how the patient reacts to insulin intakes, and would prove valuable for the predictions accuracy.
  • These parameters are called time-based data since their amount might change with time during the given cyclic time period. In other words, these parameters can be expressed as a function of time.
  • the temporal predictive model may also receive patient-related data as input.
  • the patient-related data may include a delivered insulin quantity parameter, a consumed carbohydrate quantity parameter and patient-specific parameters such an Insulin Sensitivity Factor, or a Carbohydrate-to-Insulin Ratio parameter.
  • the consumed carbohydrate quantity parameter represents a carbohydrate quantity ingested by the patient.
  • the temporal predictive model may also receive, as input, meal-related data, which may correspond to a quantity of carbohydrate ingested by the patient.
  • the patient-related data and meal-related data are variables that may have an influence on the glycemia, it may be useful to inject them in the temporal predictive model to obtain a more accurate prediction of a future glycemia.
  • the time-based data may be enough for the temporal predictive model to predict a future glycemia value. However, the prediction may be enhanced when the temporal model also receives as input the patient-related data and/or the meal-related data.
  • the temporal predictive model receives, as input, encoded temporal data.
  • the encoded temporal data represents the given cyclic time period.
  • the temporal data may be encoded according to several forms to represent the given cyclic time period.
  • time is taken into account by the predictive temporal model, such that it may help the temporal predictive model to learn glycemia temporal behaviour during a cyclic period of time.
  • the temporal predictive model may then be able to learn the patient's habits such that a glycemia behaviour will be linked to a same time, or at least a same time period, included in the given cyclic period of time.
  • a predicted value of the glycemia at a future time may then be predicted with more accuracy.
  • the given cyclic time period may correspond either to a day, a week and/or a year.
  • the temporal predictive model may be trained to determine the glycemia cyclic behaviour at each hour of the day, at each day of a week and/or at each day of a year.
  • One advantage of this configuration is that a glycemia general tendency over a relatively long period of time can be known with more accuracy than in the state of art.
  • the temporal data is encoded using a linear transformation over the given cyclic time period with a predetermined constant time step.
  • the temporal predictive model receives as inputs a sequence of time-based data over the given period of time, sampled with a constant time step, as well as one or several (e.g. hour, day of the week, day of the year) encoded temporal data corresponding to one time point of the given period of time.
  • the time point received by the temporal predictive model corresponds to the present time.
  • such encoded temporal data may take the following form: ⁇ x ⁇
  • the temporal predictive model when the present time is x, the temporal predictive model will receive x as the encoded temporal data.
  • the temporal predictive model When the present time is x+1, the temporal predictive model will receive x + 1 as the encoded temporal data.
  • At least one time-based data is sampled and inputted in the temporal predictive model at a time step between 1 minute and 20 minutes, and preferably every 5 minutes.
  • the encoded temporal data and the time-based data may not be constantly injected in the temporal predictive model at the same time.
  • the encoded temporal data may not be injected as an input in the temporal predictive model each time the temporal predictive model receives the time-based data.
  • the encoded temporal data injected in the temporal predictive model may take the following form:
  • ⁇ x ⁇ [0, 24[, where 0 is the beginning of the given cyclic period of time, meaning the first hour of a day (for example 1am), 24 (or 23 or 25 for time change) is the end of the given cyclic period of time, meaning the last hour of a day (for example midnight), and x is an hour of the day between 0 and 24. Then, the constant time step separating each point of the interval is one hour.
  • the time-based data will, for example, be injected as an input in the temporal predictive model every five minutes, while the encoded temporal data corresponding to the present time are injected as an input in the temporal predictive model every one hour.
  • the encoded temporal data injected in the temporal predictive model may take the following form: ⁇ x ⁇
  • the time-based data will, for example, be injected as an input in the temporal predictive model every five minutes, while the encoded temporal data corresponding to the present day of the week are injected as an input in the temporal predictive model every day.
  • the encoded temporal data injected in the temporal predictive model may take the following form: ⁇ x ⁇
  • the time-based data will, for example, be injected as an input in the temporal predictive model every five minutes, while the encoded temporal data corresponding to the present day of the year are injected as an input in the temporal predictive model every day.
  • more precise encoding of the temporal data could be done, for example by diminishing the time step between two points in each interval.
  • a minute of a day could be considered as a time step, in which each time points x of the considered interval would be spaced not according to an hour, as in the above described embodiment, but be spaced according to a minute to have a better and more accurate representation of the given cyclic period of time.
  • the temporal predictive model could be trained with encoded temporal data representing a night time versus a daytime, such that the temporal predictive model would be able to differentiate a glycemia cyclic temporal behaviour during day and night.
  • the temporal predictive model could also be trained with encoded temporal data representing a working day period versus a week-end or holiday period.
  • the encoded temporal data may be injected in the temporal predictive model each time the at least one time-based data is injected. This may be useful when the time-based data are irregularly sampled.
  • the temporal predictive model would be trained on several consecutive given periods of time, for example on consecutive several days.
  • several temporal data encoded according to the linear distribution described above would be injected in the temporal predictive model in conjunction with at least the time-based data, and possibly the patient-related data and the meal-related data.
  • the linear distribution of the temporal data described above is discrete, meaning that there is no continuity between the end b of a first temporal data representative of a given cyclic period of time, and the beginning a of another temporal data representative of a consecutive given cyclic period of time.
  • the last time point x of a first day would be 24 and the first time point x of a second day would be zero.
  • the predictive temporal model there is then a great difference between 24 and 0, on the contrary there is no such great difference between the last hour of a day and the first hour of a consecutive day (literally only one minute).
  • the predictive temporal model could struggle to digest that there is a continuity between two consecutive given cyclic periods of time. It is especially true for the time points near the beginning and end of each consecutive several cyclic periods of time.
  • Another embodiment of an encoding of temporal data may therefore be implemented.
  • the temporal data is encoded according to an angular distribution over the given cyclic period of time according to a predetermined constant angle interval.
  • such encoded temporal data takes the following form: (cos((x) / n * 2pi), (sin((x) / n * 2pi), where x is a time point of the given cyclic period of time and n is the total amount of time points of the given cyclic period of time. Each time point x is separated from the other by a constant angular value.
  • the temporal data is encoded according to a cosine function and a sine function. Both functions are injected in the temporal predictive model as inputs.
  • the continuity between two consecutive given cyclic time periods is ensured. More particularly, since the sine and cosine functions are continuous, the temporal data encoded according to the sine and cosine functions are also continuous. Then, the same time step separates each time point of the given cyclic period of time, as well as the last time point of the given cyclic period of time and the first time point of a consecutive cyclic period of time. In other words, the temporal data is encoded according to a continuous curve.
  • the temporal data is encoded with both the cosine and the sine functions since with this combined use they define a unique angular value, while with the use of only a cosine or a sine function, each angle could take two different values.
  • the temporal data would be encoded according to the following: (cos((x) / N * 2pi), (sin((x) / N * 2pi), where x is the hour of the day of one time point in the given cyclic period between 0 and N is the number of hours in the given day (24 or 23 or 25 for time change days).
  • each hour of a day is represented by a unique angular value.
  • the temporal data would be encoded according to the following: (cos((x) / 7 * 2pi), (sin((x) / 7 * 2pi), where x is the day of the week of one time point in the given cyclic period between 0 and 7.
  • each day of the week is represented by a unique angular value.
  • the temporal data would be encoded according to the following: (cos((x) / D * 2pi), (sin((x) / D * 2pi), where x is the day of the year of one time point in the given cyclic period between 0 and D, where D is the total number of days in the year (365 or 366).
  • each day of the year is represented by a unique angular value.
  • each time point x would correspond to a minute of a day, and each angular value would represent one minute of a day.
  • the temporal predictive model could be trained with encoded temporal data representing a night time versus a daytime, such that the temporal predictive model would be able to differentiate a glycemia cyclic temporal behaviour during day and night.
  • the temporal predictive model could also be trained with encoded temporal data representing a working day period versus a week-end period.
  • the injection of such encoded temporal data as an input to the temporal predictive model allows the temporal predictive model to determine a glycemia cyclic behaviour over this cyclic period of time.
  • temporal predictive model This can be done since both temporal data and the other inputs, i.e. at least the patient-related data and possibly the time-based data and the meal-related data, are injected in the temporal predictive model. This enables the temporal predictive model to consider and take into account the patient habits and the circadian cycle of a patient to output a predicted value of a future glycemia.
  • a person generally has the same general behaviour over a given cyclic period of time.
  • the temporal predictive model is able to link the set of data to the temporal data such that the temporal predictive model knows, eventually, how the glycemia is going to evolve during a day relative to the time of the day.
  • the same can be applied regarding a week, a month and/or a year.
  • the training of the temporal predictive model regarding the night time or the daytime can help the temporal predictive model to understand or at least consider the circadian cycle of the patient.
  • all the encoded temporal data, and more generally all the inputs of the temporal predictive model are tagged during the training of the temporal predictive model.
  • the temporal predictive model may eventually be able to discern particular times of interest over the given cyclic period of time.
  • such particular times of interest may be defined by the glycemia behaviour which evolves according to a similar pattern.
  • Such patterns and hence such particular times of interest may be linked to the patient's habits.
  • each day a patient may generally take their first meal around 8am, the second meal around 12am and the last meal around 6pm.
  • the patient may go to bed generally around 10pm and wake up around 7am.
  • the glycemia will highly increase during a meal time and may present a low variability during the sleeping time.
  • the temporal predictive model will be able to determine particular times of interest representative of meal times, in this example the particular times of interest would be around 8am, 12am and 6pm when the glycemia will highly increase.
  • the temporal predictive model may also be able to determine a particular time of interest representative of the sleeping time during which the glycemia slightly varies, in this example between 10pm and 7am.
  • This arrangement has two main technical advantages. First, the accuracy of the predicted value of glycemia is enhanced since the patient's habits are considered.
  • an action can be taken regarding the outputted predicted glycemia value and the sub period associated with it.
  • an unexpected variation in glycemia can be considered as abnormal considering the sub period in which the variation takes place.
  • Figure 3 illustrates the training of a temporal predictive model according to a first embodiment.
  • the temporal predictive model receives as inputs at least the time-based data, being a present and/or past glycemia value(s).
  • the inputs may also include patient related data and/or meal-related data.
  • the temporal predictive model receives a plurality of time-based data, and optionally patient-related data and meal-related data, of a plurality of patients.
  • the time-based data are acquired on a plurality of patients, by a data acquisition system 2.
  • the time-based data, the patient-related data and the meal-related data may be stored in a database and injected in the temporal predictive model during its training.
  • the time-based data, the patient-related data and the meal-related data of the plurality of patients come from a database in which a plurality of illustrating data are stored. These data may not have been acquired on patients but may be equivalent to acquired data, to effectively train the temporal predictive model.
  • the temporal predictive model receives as input the encoded temporal data representative of the given cyclic period of time.
  • the encoded temporal data may be encoded according to the embodiments described above.
  • the inputs of the temporal predictive model may be tagged.
  • the temporal predictive model is trained.
  • steps S1 and S2 are implemented again for the plurality of patients (step S5).
  • step S6 If the temporal predictive model has converged, it is stored at step S6.
  • Figure 4 illustrates another embodiment of the training of the temporal model.
  • the temporal predictive model receives as inputs at least the time-based data.
  • the data may also include patient related data and/or meal-related data.
  • the temporal predictive model receives a plurality of time-based data of one patient.
  • the temporal predictive model may also receive a plurality of patient-related data and/or meal-related data of the same patient.
  • the patient from which the data is injected in the temporal predictive model is the patient who wears the medical system 1 storing the temporal predictive model once it is trained.
  • the temporal predictive model receives as input the encoded temporal data representative of the given cyclic period of time.
  • the encoded temporal data may be encoded according to the embodiments described above.
  • the inputs of the temporal predictive model may be tagged.
  • the temporal predictive model is trained.
  • steps S10 and S11 are implemented again for the same patient (step S14).
  • the temporal predictive model is stored if it has converged.
  • the temporal predictive model may be stored in the memory 33 of the data processing unit 3 of the medical system 1.
  • the trained temporal predictive model may be stored on a remote server communicating with the data processing unit 3 of the medical system 1.
  • the temporal predictive model receives time-based data from the patient wearing the medical device 1.
  • the time-based data are acquired by the acquisition system 2.
  • the temporal predictive model may also receive patient-related data, inputted by the patient via the user interface.
  • the temporal predictive model also receives the encoded temporal data.

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EP22177106.6A 2022-06-02 2022-06-02 Gerät, computergestütztes verfarhen, medizinisches system zur bestimmung eines vorhergesaten glykämiewerts Pending EP4287210A1 (de)

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EP22177106.6A EP4287210A1 (de) 2022-06-02 2022-06-02 Gerät, computergestütztes verfarhen, medizinisches system zur bestimmung eines vorhergesaten glykämiewerts
US18/324,716 US20230395266A1 (en) 2022-06-02 2023-05-26 Device, computerized method, medical system for determining a predicted value of glycemia

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US20150289821A1 (en) * 2014-04-10 2015-10-15 Dexcom, Inc. Glycemic urgency assessment and alerts interface
US20210390399A1 (en) * 2018-11-01 2021-12-16 Imperial College Of Science, Technology & Medicine Predicting Physiological Parameters
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